A neural network model based on reinforcement learning is investigated for use as a shipboard autonomous channel navigator. The model used consists of two neuron-like elements. The basic learning scheme involves learning with a critic. The network consists of an adaptive critic element (ACE) and an adaptive search element (ASE). The ASE explores the channel region while the ACE criticizes the actions of the ASE and tries to predict failures of the ASE's attempt to navigate.The neural network model developed has been shown to be useful through software simulation with graphical feedback. A similar implementation could have applications in many electronic mapping systems utilizing vector information. This paper investigates the performance of such a system and its adaptability to new channels.